Inflammatory Biomarkers in Childhood Arterial Ischemic Stroke
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Among children with arterial ischemic stroke (AIS), those with arteriopathy have the highest recurrence risk. We hypothesized that arteriopathy progression is an inflammatory process and that inflammatory biomarkers would predict recurrent AIS. METHODS: In an international study of childhood AIS, we selected cases classified into 1 of the 3 most common childhood AIS causes: definite arteriopathic (n=103), cardioembolic (n=55), or idiopathic (n=78). We measured serum concentrations of high-sensitivity C-reactive protein, serum amyloid A, myeloperoxidase, and tumor necrosis factor-α. We used linear regression to compare analyte concentrations across the subtypes and Cox proportional hazards models to determine predictors of recurrent AIS. RESULTS: Median age at index stroke was 8.2 years (interquartile range, 3.6-14.3); serum samples were collected at median 5.5 days post stroke (interquartile range, 3-10 days). In adjusted models (including age, infarct volume, and time to sample collection) with idiopathic as the reference, the cardioembolic (but not arteriopathic) group had higher concentrations of high-sensitivity C-reactive protein and myeloperoxidase, whereas both cardioembolic and arteriopathic groups had higher serum amyloid A. In the arteriopathic (but not cardioembolic) group, higher high-sensitivity C-reactive protein and serum amyloid A predicted recurrent AIS. Children with progressive arteriopathies on follow-up imaging had higher recurrence rates, and a trend toward higher high-sensitivity C-reactive protein and serum amyloid A, compared with children with stable or improved arteriopathies. CONCLUSIONS: Among children with AIS, specific inflammatory biomarkers correlate with cause and-in the arteriopathy group-risk of stroke recurrence. Interventions targeting inflammation should be considered for pediatric secondary stroke prevention trials.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".